Secure AI Application Development: A Practical Checklist for Agents and RAG

Secure AI application development starts with a simple premise: a model response, a user prompt, and a retrieved document are all untrusted inputs to your system. The application—not the model—must enforce identity, permissions, business rules, and safe handling of data.

This matters most when an AI feature can search internal knowledge, access customer accounts, call an API, generate content for publication, or take an action. Good security does not make the product less useful; it defines clear boundaries that make useful automation safe enough to run in production.

The AI risks product teams should plan for

OWASP's 2025 LLM Top 10 includes prompt injection, sensitive-information disclosure, supply-chain issues, data and model poisoning, improper output handling, excessive agency, system-prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. The categories are a practical checklist for product decisions, not merely a security team's concern.

For example, retrieval-augmented generation can improve answer quality but does not neutralize hostile instructions hidden in a document. A customer support agent that can look up an order should not gain the power to cancel it just because a prompt asks persuasively.

A secure AI application development checklist

Keep authorization outside the model

Derive user and tenant identity from the authenticated session. Enforce access in the database, API, and retrieval layer before information becomes model context. Do not let natural-language text select another account, document collection, or permission level.

Use least-privilege tools

Give an agent discrete, typed tools such as get_current_order_status or create_support_draft, not broad database or shell access. Validate parameters on the server, use idempotency for writes, and require explicit confirmation for money movement, deletion, deployment, or sensitive changes.

Treat retrieved data as untrusted

Preserve document provenance, ownership, freshness, and access labels. Screen content where appropriate, delimit it from trusted instructions, and ensure retrieved text cannot alter tool policy. Test with malicious and irrelevant documents as well as normal ones.

Validate inputs and outputs

Use schema validation for structured results, encode generated content safely for its destination, and prevent model text from becoming executable code, markup, database queries, or security decisions without a deterministic check.

Build observability and response paths

Log the necessary event trail: model selection, input class, retrieved source IDs, tools proposed and executed, approvals, errors, and user feedback. Set rate and cost limits. Provide a human escalation and a way to disable a problematic workflow quickly.

Test before and after launch

Create a recurring evaluation set with normal requests, edge cases, permission boundaries, prompt-injection attempts, stale documents, high-volume behavior, and failure recovery. NIST's Generative AI Profile encourages organizations to govern, map, measure, and manage AI risk across the lifecycle; that is a better operating model than a one-time security review.

App Commandos builds security and operational controls into web application development and AI integrations from the first release. Contact us if you need a security-aware AI product roadmap.

FAQ

Is RAG secure by default?

No. It requires authorization filters, content governance, source tracing, prompt-injection testing, and safe tool boundaries.

Should AI actions always require a human?

Read-only and low-risk actions may be automated after testing. Irreversible, financial, sensitive, or uncertain actions should have confirmation or human review.